1. Field of the Invention
The present invention relates generally to iterative interference cancellation in received wireless communication signals and, more particularly, to cancellation of intra-cell interference and/or inter-cell interference in coded spread spectrum communication systems.
2. Discussion of the Related Art
In an exemplary wireless multiple-access system, a communication resource is divided into code-space subchannels that are allocated to different users. A plurality of subchannel signals received by a wireless terminal (e.g., a subscriber unit or a base station) may correspond to different users and/or different subchannels allocated to a particular user.
If a single transmitter broadcasts different messages to different receivers, such as a base station in a wireless communication system broadcasting to a plurality of mobile terminals, the channel resource is subdivided in order to distinguish between messages intended for each mobile. Thus, each mobile terminal, by knowing its allocated subchannel(s), may decode messages intended for it from the superposition of received signals. Similarly, a base station typically separates received signals into subchannels in order to differentiate between users.
In a multipath environment, received signals are superpositions of time-delayed and complex-scaled versions of the transmitted signals. Multipath can cause several types of interference. Intra-channel interference occurs when the multipath time-delays cause subchannels to leak into other subchannels. For example, in a forward link, subchannels that are orthogonal at the transmitter may not be orthogonal at the receiver. When multiple base stations (or sectors or cells) are active, there may also be inter-channel interference caused by unwanted signals received from other base stations. Each of these types of interference can degrade communications by causing a receiver to incorrectly decode received transmissions, thus increasing a receiver's error floor. Interference may also have other deleterious effects on communications. For example, interference may lower capacity in a communication system, decrease the region of coverage, and/or decrease maximum data rates. For these reasons, a reduction in interference can improve reception of selected signals while addressing the aforementioned limitations due to interference.
These interferences take the following form when code division multiplexing is employed for a communication link, either with code division multiple access (as used in CDMA 2000, WCDMA, and related standards) or with time division multiple access (as used in EV-DO and related standards). A set of symbols is sent across a common time-frequency slot of the physical channel and separated using a set of distinct code waveforms, which are usually chosen to be orthogonal (or pseudo-orthogonal for reverse-link transmissions). The code waveforms typically vary in time, and these variations are introduced by a pseudo-random spreading code (PN sequence). The wireless transmission medium is characterized by a time-varying multipath profile that causes multiple time-delayed replicas of the transmitted waveform to be received, each replica having a distinct amplitude and phase due to path loss, absorption, and other propagation effects. As a result, the received code set is no longer orthogonal. The code space suffers from intra-channel interference within a base station as well as inter-channel interference arising from transmissions in adjacent cells.
The most basic receiver architecture employed to combat these various effects is the well-known Rake receiver. The Rake receiver uses a channel-tracking algorithm to resolve the received signal energy onto various multipath delays. These delayed signals are then weighted by the associated complex channel gains (which may be normalized by path noise powers) and summed to form a single resolved signal, which exploits some of the path diversity available from the multipath channel. It is well known that the Rake receiver suffers from a significant interference floor, which is due to both self-interference from the base station of interest (or base stations, when the mobile is in a soft-handoff base station diversity mode) and multiple-access interference from all base stations in the coverage area. This interference limits the maximum data rates achievable by the mobiles within a cell and the number of mobiles that can be supported in the cell.
Advanced receivers have been proposed to overcome the limitations of the Rake receiver. The optimal multi-user detector (MUD) has the best performance, but is generally too computationally complex to implement. MUD complexity increases exponentially with respect to the total number of active subchannels across the cell of interest and the interfering cells as well as the constellation size(s) of the subchannels. This complexity is so prohibitive that even efficient implementations based on the Viterbi algorithm cannot make it manageable in current hardware structures. Another approach is a properly designed linear receiver, which in many channel scenarios, is able to retain much of the optimal MUD performance, but with a complexity that is polynomial in the number of subchannels. The most common examples are the linear minimum mean squared error (LMMSE) receiver and the related decorrelating (or zero-forcing) receiver, which both require finding, or approximating, the inverse of a square matrix whose dimension is equal to the lesser between the number of active subchannels and the length (in samples) of the longest spreading code.
Complexity can still be prohibitive with these receivers, because such a matrix inverse needs to be calculated (or approximated) for each symbol. These receivers depend not only on the spectral characteristics of the multipath fading channel (which could be slowly time varying), but also on the time-varying spreading codes employed on the subchannels over each symbol. Thus, these receivers vary at the symbol rate even if the channel varies much more slowly.
An alternative approach currently under development for advance receivers sidesteps the need to invert a matrix for each symbol. It accomplishes this by employing a PN-averaged LMMSE (PNA-LMMSE) receiver that assumes the PN code is random and unknown at the receiver (at least for determining the correlation matrix). While this receiver is generally inferior to the LMMSE approach, it has the advantage of not having to be implemented directly, because it is amenable to adaptive (or partially adaptive) implementations. The advantages of an adaptive implementation over a direct implementation include reduced complexity and the fact that the additive noise power (i.e., background RF radiation specific to the link environment, noise in the receiver's RF front end, and any processing noise such as noise due to quantization and imperfect filtering) does not have to be estimated. However, these advantages incur the costs associated with adaptive filters (e.g., performance and adaptation rate). Note that a direct implementation without knowledge of the noise power modifies the LMMSE and PNA-LMMSE receivers into the corresponding decorrelating (or zero-forcing) receivers that arise from taking the background noise power to be zero when deriving the LMMSE and PNA-MMSE receivers.
Another method for further reducing complexity is to iteratively approximate the matrix-inverse functionality of the LMMSE receiver without explicitly calculating the inverse. Receivers of this type employ multistage interference cancellation. One particular type is known as parallel interference cancellation (PIC), and is motivated by well-known iterative techniques of quadratic minimization. In each stage of PIC, the data symbols of the subchannels are estimated. For each subchannel, an interference signal from the other subchannels is synthesized, followed by interference cancellation that subtracts the synthesized interference from each subchannel. The interference-cancelled subchannels are then fed to a subsequent PIC stage. Ideally, within just a few stages (i.e., before the complexity grows too large), the performance rivals that of the full linear receiver using a matrix inverse.
PIC can be implemented in various modes depending on what types of symbol estimates are used for interference cancellation. In a soft-cancellation mode, PIC does not exploit additional information inherent in the finite size of user constellations. That is, estimates of data symbols are not quantized to a constellation point when constructing interference signals. However, in some multiple-access schemes, the user constellations may be known (e.g., in an EV-DO link or in a WCDMA link without HSDPA users) or determined through a modulation classifier. In such cases, it is possible for PIC to be implemented in a hard-cancellation mode. That is, estimates of data symbols are quantized to constellation points (i.e., hard decisions) when constructing the interference signal.
In a mixed-cancellation mode, PIC employs a soft decision on each symbol whose constellation is unknown, and either a soft or hard decision on each symbol whose constellation is known, depending on how close the soft estimate is to the hard decision. Such a mixed-decision PIC typically outperforms both the soft-decision PIC and the hard-decision PIC. Moreover, it can also substantially outperform the optimal LMMSE receiver and promises even greater performance gains over PNA-LMMSE approaches currently under development for advanced receivers. The performance of soft-decision PIC is bounded by the optimal LMMSE.